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Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation
A. Howard, A. Zhmoginov, L.-C. Chen, M. Sandler, and M. Zhu · 2018
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Deep mutual learning
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu · 2018
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LEAF: A benchmark for federated settings
S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2019
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Searching for MobileNetV3
A. Howard, M. Sandler, G. Chu, L. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam · 2019
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FedMD: Heterogenous Federated Learning via Model Distillation
D. Li and J. Wang · 2019
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Once for All: Train one network and specialize it for efficient deployment
H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han · 2020
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Personalized federated learning: A meta-learning approach
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2020
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Lower bounds and optimal algorithms for personalized federated learning
F. Hanzely, S. Hanzely, S. Horváth, and P. Richtárik · 2020
Cited alongside, same era.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
C. He, M. Annavaram, and S. Avestimehr · 2020
Cited alongside, same era.
C. He, M. Annavaram, and S. Avestimehr · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. J. Reddi, S. U. Stich, and A. T. Suresh · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
Cited alongside, same era.
Personalized federated learning with moreau envelopes
C. T. Dinh, N. H. Tran, and T. D. Nguyen · 2021
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Advances and open problems in federated learning
P. K. et al · 2021
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Personalized cross-silo federated learning on non-iid data
Y. Huang, L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y. Zhang · 2021
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Enabling nas with automated super-network generation
J. P. Munoz, N. Lyalyushkin, Y. Akhauri, A. Senina, A. Kozlov, and N. Jain · 2021
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Adaptive dynamic pruning for non-iid federated learning
S. Yu, P. Nguyen, A. Anwar, and A. Jannesari · 2021
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T. Lin, L. Kong, S. U. Stich, and M. Jaggi · 2020
Cited alongside, same era.
FedED: Federated learning via ensemble distillation for medical relation extraction
D. Sui, Y. Chen, J. Zhao, Y. Jia, Y. Xie, and W. Sun · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor · 2020
Cited alongside, same era.
Federated neural architecture search
M. Xu, Y. Zhao, K. Bian, G. Huang, Q. Mei, and X. Liu · 2020
Cited alongside, same era.
Salvaging federated learning by local adaptation
T. Yu, E. Bagdasaryan, and V. Shmatikov · 2020
Cited alongside, same era.
Personalized federated learning with first order model optimization
M. Zhang, K. Sapra, S. Fidler, S. Yeung, and J. M. Alvarez · 2021
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Federated learning on non-iid data silos: An experimental study
Q. Li, Y. Diao, Q. Chen, and B. He · 2022
Closest in time.
Federated Knowledge Distillation
H. Seo, J. Park, S. Oh, M. Bennis, and S.-L. Kim · 2022
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Communication-efficient federated learning via knowledge distillation
C. Wu, F. Wu, L. Lyu, Y. Huang, and X. Xie · 2022
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SPATL: Salient parameter aggregation and transfer learning for heterogeneous federated learning
S. Yu, P. Nguyen, W. Abebe, W. Qian, A. Anwar, and A. Jannesari · 2022
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